Job Description

:
This role is for one of the Weekday's clients
Salary range: Rs 6000000 - Rs 7000000 (ie INR 60-70 LPA)
Min Experience: 8 years
Location: Noida, Uttar Pradesh, India
JobType: full-time
We are seeking an experienced MLOps Engineer to design, build, and operate scalable, production-grade machine learning platforms and pipelines. This role focuses on operationalizing ML workflows across cloud-native infrastructure, enabling reliable model training, deployment, monitoring, and lifecycle management at scale. You will collaborate closely with data science, data engineering, and platform teams to bring ML solutions into production efficiently and securely.
Requirements:
Key Responsibilities
ML Platform & Pipeline Engineering

• Design, implement, and manage cloud-native ML platforms supporting model training, inference, and lifecycle automation.
• Build and orchestrate ML and ETL pipelines using Apache Airflow / AWS MWAA and distributed processing frameworks such as Apache Spark (EMR/Glue).
• Productionize data science workflows, experiments, and notebooks into robust, scalable systems.

Deployment & Automation

• Containerize and deploy ML workloads using Docker, Kubernetes (EKS), ECS/Fargate, and AWS Lambda.
• Develop and maintain CI/CT/CD pipelines for automated model training, validation, testing, and deployment.
• Enable reproducible ML environments using containerized development workflows and Jupyter-based platforms.

Observability, Governance & Reliability

• Implement ML observability including data drift, model drift, performance monitoring, and alerting using CloudWatch, Prometheus, and Grafana.
• Ensure data governance, metadata management, versioning, lineage, and reproducibility across ML pipelines.
• Build secure, reliable, and scalable data pipelines aligned with best practices.

Collaboration & Enablement

• Work closely with data scientists to operationalize ML models and experiments.
• Partner with data engineering and platform teams to scale ML infrastructure and workflows.
• Support best practices around ML lifecycle management and operational excellence.

Required Skills & Experience

• 8+ years of experience in MLOps, DevOps, or ML platform engineering.
• Strong hands-on expertise with AWS, including:

• Compute & Orchestration: EKS, ECS, EC2, Lambda
• Data Services: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow Orchestration: Airflow / MWAA, Step Functions
• Monitoring & Logging: CloudWatch, OpenSearch, Grafana
• Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong experience with Docker, Kubernetes, Git, and CI/CD tools (GitHub Actions, Jenkins, etc.).
• Solid Linux, scripting, automation, and troubleshooting skills.
• Experience enabling reproducible ML environments and containerized development workflows.

Education

• Master's degree in Computer Science, Machine Learning, Data Engineering, or a related field.

Skills
MLOps, AWS, Airflow/MWAA, Apache Spark, Kubernetes, Docker, CI/CT/CD, CloudWatch, Glue, ECS

Skills Required